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Unverified Commit 9684677a authored by arai713's avatar arai713 Committed by GitHub
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Merge branch 'develop' into hip_tensor_permute

parents 36f6966a 98fd41f5
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library/src/tensor_operation_instance/gpu/image_to_column/device_image_to_column_ndhwgc_3d_instance.cpp 0 → 100644
View file @ 9684677a
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_image_to_column_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using namespace ck::conv_tensor_rearrange_op;
void add_device_image_to_column_ndhwgc_3d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, BF16, BF16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_BF16
add_device_operation_instances(instances, device_image_to_column_bf16_instances<3, NDHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_ndhwgc_3d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F16, F16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP16
add_device_operation_instances(instances, device_image_to_column_f16_instances<3, NDHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_ndhwgc_3d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, F32, F32, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP32
add_device_operation_instances(instances, device_image_to_column_f32_instances<3, NDHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_ndhwgc_3d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<3, NDHWGC, int8_t, int8_t, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_INT8
add_device_operation_instances(instances, device_image_to_column_i8_instances<3, NDHWGC>{});
#else
ignore = instances;
#endif
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/image_to_column/device_image_to_column_nhwgc_2d_instance.cpp 0 → 100644
View file @ 9684677a
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_image_to_column_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using namespace ck::conv_tensor_rearrange_op;
void add_device_image_to_column_nhwgc_2d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, BF16, BF16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_BF16
add_device_operation_instances(instances, device_image_to_column_bf16_instances<2, NHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nhwgc_2d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F16, F16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP16
add_device_operation_instances(instances, device_image_to_column_f16_instances<2, NHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nhwgc_2d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, F32, F32, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP32
add_device_operation_instances(instances, device_image_to_column_f32_instances<2, NHWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nhwgc_2d_i8_instances(
std::vector<
std::unique_ptr<DeviceConvTensorRearrange<2, NHWGC, int8_t, int8_t, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_INT8
add_device_operation_instances(instances, device_image_to_column_i8_instances<2, NHWGC>{});
#else
ignore = instances;
#endif
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/image_to_column/device_image_to_column_nwgc_1d_instance.cpp 0 → 100644
View file @ 9684677a
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange/device_image_to_column_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using namespace ck::conv_tensor_rearrange_op;
void add_device_image_to_column_nwgc_1d_bf16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, BF16, BF16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_BF16
add_device_operation_instances(instances, device_image_to_column_bf16_instances<1, NWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nwgc_1d_f16_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F16, F16, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP16
add_device_operation_instances(instances, device_image_to_column_f16_instances<1, NWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nwgc_1d_f32_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, F32, F32, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_FP32
add_device_operation_instances(instances, device_image_to_column_f32_instances<1, NWGC>{});
#else
ignore = instances;
#endif
}
void add_device_image_to_column_nwgc_1d_i8_instances(
std::vector<std::unique_ptr<DeviceConvTensorRearrange<1, NWGC, int8_t, int8_t, ImageToColumn>>>&
instances)
{
#ifdef CK_ENABLE_INT8
add_device_operation_instances(instances, device_image_to_column_i8_instances<1, NWGC>{});
#else
ignore = instances;
#endif
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/README.md
View file @ 9684677a
......@@ -50,21 +50,23 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s
## Profile contraction kernels
```bash
#arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear)
#arg2: data type (0: fp32; 1: f64)\n"
#arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
#arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1])
#arg4: verification (0: no; 1: yes)
#arg5: initialization (0: no init; 1: integer value; 2: decimal value)
#arg6: print tensor value (0: no; 1: yes)
#arg7: time kernel (0: no, 1: yes)
#arg8 and arg9: alpha and beta
#arg10 to 15: M0, M1, N0, N1, K0, K1
#arg16 to 31: Strides for A, B, D and E (skip for default)
################ op datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
./bin/ckProfiler contraction_bilinear 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
#arg5: verification (0: no; 1: yes)
#arg6: initialization (0: no init; 1: integer value; 2: decimal value)
#arg7: print tensor value (0: no; 1: yes)
#arg8: time kernel (0: no, 1: yes)
#arg9: alpha
#arg10: beta
#arg11 to 16: M0, M1, N0, N1, K0, K1
#arg17 to 32: Strides for A, B, D and E (skip for default)
################ op datatype compute_datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
./bin/ckProfiler contraction_bilinear 0 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
```
Result (MI100)
......@@ -194,7 +196,8 @@ Note: This kernel use atomic add, this will cause output buffer to be accumulate
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight bf16, Output bf16
# 3: Input int8, Weight int8, Output int8)
# arg3: tensor layout (0: Input[N, Hi, Wi, C], Output[N * Ho * Wo, Y * X * C])
# arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],
# 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
......
profiler/include/profiler/profile_contraction_impl.hpp
View file @ 9684677a
......@@ -31,10 +31,14 @@ namespace profiler {
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale;
using F32 = float;
using F64 = double;
template <typename ALayout,
typename BLayout,
typename CDELayout,
typename DataType,
typename ComputeDataType,
typename DTupleDataType,
typename CDElementOp>
int profile_contraction_impl(ck::index_t do_verification,
......@@ -45,10 +49,10 @@ int profile_contraction_impl(ck::index_t do_verification,
const std::vector<ck::index_t>& M,
const std::vector<ck::index_t>& N,
const std::vector<ck::index_t>& K,
const std::vector<ck::index_t>& StridesA,
const std::vector<ck::index_t>& StridesB,
const std::vector<ck::index_t>& StridesE,
const std::vector<ck::index_t>& StridesD)
const std::vector<ck::index_t>& StridesA, // [M0, M1, K0, K1]
const std::vector<ck::index_t>& StridesB, // [N0, N1, K0, K1]
const std::vector<ck::index_t>& StridesE, // [M0, M1, N0, N1]
const std::vector<ck::index_t>& StridesD) // [M0, M1, N0, N1]
{
bool pass = true;
......@@ -63,13 +67,13 @@ int profile_contraction_impl(ck::index_t do_verification,
};
Tensor<DataType> a_m_k(f_host_tensor_descriptor(M, K, StridesA));
Tensor<DataType> b_k_n(f_host_tensor_descriptor(K, N, StridesB));
Tensor<DataType> b_n_k(f_host_tensor_descriptor(N, K, StridesB));
Tensor<DataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> d_m_n(f_host_tensor_descriptor(M, N, StridesD));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "b_n_k: " << b_n_k.mDesc << std::endl;
std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
......@@ -78,12 +82,12 @@ int profile_contraction_impl(ck::index_t do_verification,
case 0: break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
b_n_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
d_m_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
b_n_k.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
d_m_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
}
......@@ -91,12 +95,12 @@ int profile_contraction_impl(ck::index_t do_verification,
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
DeviceMem a_device_buf(sizeof(DataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(DataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(DataType) * b_n_k.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(DataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DataType) * d_m_n.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
b_device_buf.ToDevice(b_n_k.mData.data());
e_device_buf.SetZero();
d_device_buf.ToDevice(d_m_n.mData.data());
......@@ -118,7 +122,8 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType,
AElementOp,
BElementOp,
CDElementOp>;
CDElementOp,
ComputeDataType>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......@@ -126,6 +131,9 @@ int profile_contraction_impl(ck::index_t do_verification,
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
using AccDataType =
typename std::conditional<std::is_same<ComputeDataType, F64>::value, F64, F32>::type;
// Run reference op
if(do_verification)
{
......@@ -136,7 +144,8 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType,
DataType,
DataType,
DataType,
AccDataType,
ComputeDataType,
AElementOp,
BElementOp>;
......@@ -146,7 +155,7 @@ int profile_contraction_impl(ck::index_t do_verification,
Tensor<DataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
auto ref_argument =
ref_op.MakeArgument(a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op);
ref_op.MakeArgument(a_m_k, b_n_k, c_m_n_host_result, a_element_op, b_element_op);
ref_invoker.Run(ref_argument);
......@@ -272,8 +281,29 @@ int profile_contraction_impl(ck::index_t do_verification,
{
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
float threshold =
static_cast<DataType>(nelems_k) * std::numeric_limits<DataType>::epsilon();
// Both the kernel and the reference use `AccDataType`, so an absolute error of both
// of them is bounded by `nelems_k * std::numeric_limits<AccDataType>::epsilon()`.
// Comparing one to another can result in an absolute error as high as twice that
// value.
double threshold = 2 * nelems_k * std::numeric_limits<AccDataType>::epsilon();
// Handle the possible casting error of either AccDataType -> DataType or
// DataType -> ComputeDataType.
// TODO: Add a generic solution for calculating thresholds in CK.
if constexpr(ck::is_same_v<DataType, ck::bhalf_t> ||
ck::is_same_v<ComputeDataType, ck::bhalf_t>)
{
const double epsilon = std::pow(2, -7);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
else if constexpr(ck::is_same_v<DataType, ck::half_t> ||
ck::is_same_v<ComputeDataType, ck::half_t>)
{
const double epsilon = std::pow(2, -10);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
pass = pass & ck::utils::check_err(e_m_n_device_result,
e_m_n_host_result,
"Error: incorrect results!",
......@@ -283,7 +313,7 @@ int profile_contraction_impl(ck::index_t do_verification,
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_n_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
......
profiler/include/profiler/profile_contraction_utils.hpp
View file @ 9684677a
......@@ -23,8 +23,18 @@ enum struct ContractionMatrixLayout
enum struct ContractionDataType
{
F32_F32_F32_F32, // 0
F64_F64_F64_F64, // 1
F32_F32_F32_F32, // 0
F64_F64_F64_F64, // 1
F16_F16_F16_F16, // 2
BF16_BF16_BF16_BF16, // 3
};
enum struct ContractionComputeDataType
{
F32 = 0,
F64,
F16,
BF16,
};
inline void collect_index_params(char* argv[],
......
profiler/include/profiler/profile_conv_tensor_rearrange_impl.hpp
View file @ 9684677a
......@@ -93,6 +93,26 @@ static auto make_ref_op()
}
}
template <typename InputLayout>
static auto create_gemm_desc(const ck::index_t G, const ck::index_t NDoHoWo, const ck::index_t CZYX)
{
using namespace ck::tensor_layout::convolution;
if constexpr(std::is_same_v<InputLayout, GNWC> || std::is_same_v<InputLayout, GNHWC> ||
std::is_same_v<InputLayout, GNDHWC>)
{
return HostTensorDescriptor({G, NDoHoWo, CZYX});
}
else if constexpr(std::is_same_v<InputLayout, NWGC> || std::is_same_v<InputLayout, NHWGC> ||
std::is_same_v<InputLayout, NDHWGC>)
{
return HostTensorDescriptor({G, NDoHoWo, CZYX}, {CZYX, CZYX * G, 1});
}
else
{
throw std::runtime_error("Unsupported layout!");
}
}
template <index_t NDimSpatial,
typename InputLayout,
typename InputDataType,
......@@ -116,13 +136,13 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
const auto image_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InputLayout>(
conv_param);
const auto gemm_desc = HostTensorDescriptor({NDoHoWo, CZYX});
const auto gemm_desc = create_gemm_desc<InputLayout>(conv_param.G_, NDoHoWo, CZYX);
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
std::array<ck::index_t, 2> gemm_m_k_strides{};
std::array<ck::index_t, 3> gemm_g_m_k_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
......@@ -134,7 +154,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
copy(conv_param.filter_spatial_lengths_, filter_spatial_lengths);
copy(conv_param.output_spatial_lengths_, output_spatial_lengths);
copy(image_desc.GetStrides(), image_g_n_c_wis_strides);
copy(gemm_desc.GetStrides(), gemm_m_k_strides);
copy(gemm_desc.GetStrides(), gemm_g_m_k_strides);
copy(conv_param.conv_filter_strides_, conv_filter_strides);
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
copy(conv_param.input_left_pads_, input_left_pads);
......@@ -212,13 +232,14 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<InputDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<OutputDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_,
conv_param.C_,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
image_g_n_c_wis_strides,
gemm_m_k_strides,
gemm_g_m_k_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
......@@ -234,7 +255,7 @@ bool profile_conv_tensor_rearrange_impl(int do_verification,
float avg_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_btype =
NDoHoWo * CZYX * (sizeof(OutputDataType) + sizeof(InputDataType));
conv_param.G_ * NDoHoWo * CZYX * (sizeof(OutputDataType) + sizeof(InputDataType));
float gb_per_sec = num_btype / 1.E6 / avg_time;
std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
<< op_name << std::endl;
......
profiler/include/profiler/profile_gemm_impl.hpp
View file @ 9684677a
......@@ -75,8 +75,8 @@ int profile_gemm_impl(int do_verification,
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 0.1});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.05, 0.05});
}
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
......
profiler/include/profiler/profile_gemm_splitk_impl.hpp
View file @ 9684677a
......@@ -143,8 +143,7 @@ bool profile_gemm_splitk_impl(int do_verification,
// profile device GEMM instances
for(auto& op_ptr : op_ptrs)
{
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 36, 40, 60,
64, 72, 80, 88, 96, 128, 144, 160, 176, 192, 256};
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 32, 36, 40, 64, 96, 128};
if(KBatch > 0)
{
......
profiler/src/CMakeLists.txt
View file @ 9684677a
......@@ -28,9 +28,11 @@ set(PROFILER_SOURCES
profile_grouped_conv_bwd_data.cpp
profile_conv_tensor_rearrange.cpp
)
if(DL_KERNELS)
list(APPEND PROFILER_SOURCES profile_batched_gemm_multi_d.cpp)
endif()
if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
list(APPEND PROFILER_SOURCES profile_batched_gemm_gemm.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_fastgelu.cpp)
......@@ -110,4 +112,5 @@ if(DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_instance)
target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_gemm_fastgelu_instance)
endif()
rocm_install(TARGETS ${PROFILER_EXECUTABLE} COMPONENT profiler)
profiler/src/profile_contraction_bilinear.cpp
View file @ 9684677a
......@@ -17,8 +17,9 @@
static void print_helper_msg()
{
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64)\n"
<< "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
<< "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
......@@ -26,40 +27,42 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg4: verification (0: no; 1: yes)\n"
<< "arg5: initialization (0: no init; 1: integer value; 2: decimal "
<< "arg5: verification (0: no; 1: yes)\n"
<< "arg6: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n"
<< "arg8 and arg9: alpha and beta\n"
<< "arg10 to 15: M0, M1, N0, N1, K0, K1\n"
<< "arg16 to 31: Strides for A, B, D and E (skip for default)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n"
<< "arg9: alpha\n"
<< "arg10: beta\n"
<< "arg11 to 16: M0, M1, N0, N1, K0, K1\n"
<< "arg17 to 32: Strides for A, B, D and E (skip for default)\n"
<< std::endl;
}
int profile_contraction_bilinear(int argc, char* argv[])
{
const bool default_strides = argc == 16;
const bool default_strides = argc == 17;
if(argc != 32 && argc != 16)
if(argc != 33 && argc != 17)
{
print_helper_msg();
exit(1);
}
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const ck::index_t init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const float alpha = std::stof(argv[8]);
const float beta = std::stof(argv[9]);
const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const ck::index_t init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const float alpha = std::stof(argv[9]);
const float beta = std::stof(argv[10]);
std::vector<ck::index_t> M;
std::vector<ck::index_t> N;
std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 10;
const ck::index_t dims_arg_num = 11;
collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2);
......@@ -76,90 +79,130 @@ int profile_contraction_bilinear(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
}
using F32 = float;
using F64 = double;
auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) {
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
using DataType = decltype(type);
if(default_strides)
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using F64 = double;
auto profile =
[&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) {
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<DataType>,
Bilinear>(do_verification,
init_method,
do_log,
time_kernel,
Bilinear{alpha, beta},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
return profile(Row{}, Row{}, Row{}, type, compute_type);
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ck::Tuple<DataType>,
Bilinear>(do_verification,
init_method,
do_log,
time_kernel,
Bilinear{alpha, beta},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, type, compute_type);
}
return false;
};
if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
if(data_type == ContractionDataType::F32_F32_F32_F32)
{
return profile(Row{}, Col{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F32{}, F32{});
}
else if(compute_data_type == ContractionComputeDataType::F16)
{
return run_profile_for_datatype(F32{}, F16{});
}
else if(compute_data_type == ContractionComputeDataType::BF16)
{
return run_profile_for_datatype(F32{}, BF16{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
else if(data_type == ContractionDataType::F64_F64_F64_F64)
{
return profile(Col{}, Row{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F64)
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
else if(data_type == ContractionDataType::F16_F16_F16_F16)
{
return profile(Col{}, Col{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else
else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16)
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(BF16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_bilinear);
profiler/src/profile_contraction_scale.cpp
View file @ 9684677a
......@@ -17,8 +17,9 @@
static void print_helper_msg()
{
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64)\n"
<< "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
<< "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n"
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
......@@ -26,39 +27,40 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg4: verification (0: no; 1: yes)\n"
<< "arg5: initialization (0: no init; 1: integer value; 2: decimal "
<< "arg5: verification (0: no; 1: yes)\n"
<< "arg6: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n"
<< "arg8: alpha\n"
<< "arg9 to 14: M0, M1, N0, N1, K0, K1\n"
<< "arg15 to 30: Strides for A, B, D and E (skip for default)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n"
<< "arg9: alpha\n"
<< "arg10 to 15: M0, M1, N0, N1, K0, K1\n"
<< "arg16 to 31: Strides for A, B, D and E (skip for default)\n"
<< std::endl;
}
int profile_contraction_scale(int argc, char* argv[])
{
const bool default_strides = argc == 15;
const bool default_strides = argc == 16;
if(argc != 31 && argc != 15)
if(argc != 32 && argc != 16)
{
print_helper_msg();
exit(1);
}
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const ck::index_t init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const float alpha = std::stof(argv[8]);
const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4]));
const bool do_verification = std::stoi(argv[5]);
const ck::index_t init_method = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]);
const float alpha = std::stof(argv[9]);
std::vector<ck::index_t> M;
std::vector<ck::index_t> N;
std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 9;
const ck::index_t dims_arg_num = 10;
collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2);
......@@ -75,88 +77,131 @@ int profile_contraction_scale(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
}
using F32 = float;
using F64 = double;
auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) {
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
using DataType = decltype(type);
if(default_strides)
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using F64 = double;
auto profile =
[&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) {
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout);
using CDELayout = decltype(cde_layout);
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<>,
Scale>(do_verification,
init_method,
do_log,
time_kernel,
Scale{alpha},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
return profile(Row{}, Row{}, Row{}, type, compute_type);
}
bool pass = ck::profiler::
profile_contraction_impl<ALayout, BLayout, CDELayout, DataType, ck::Tuple<>, Scale>(
do_verification,
init_method,
do_log,
time_kernel,
Scale{alpha},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, type, compute_type);
}
return false;
};
if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
if(data_type == ContractionDataType::F32_F32_F32_F32)
{
return profile(Col{}, Row{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, F32{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F32{}, F32{});
}
else if(compute_data_type == ContractionComputeDataType::F16)
{
return run_profile_for_datatype(F32{}, F16{});
}
else if(compute_data_type == ContractionComputeDataType::BF16)
{
return run_profile_for_datatype(F32{}, BF16{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
else if(data_type == ContractionDataType::F64_F64_F64_F64)
{
return profile(Col{}, Row{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F64)
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
else if(data_type == ContractionDataType::F16_F16_F16_F16)
{
return profile(Col{}, Col{}, Row{}, F64{});
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
else
else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16)
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(BF16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
}
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale);
profiler/src/profile_conv_tensor_rearrange.cpp
View file @ 9684677a
......@@ -19,7 +19,8 @@ enum struct RearrangeOp
enum struct ConvLayout
{
NHWC, // 0
GNHWC, // 0
NHWGC, // 1
};
enum struct DataType
......@@ -42,7 +43,8 @@ static void print_helper_msg()
<< " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight bf16, Output bf16\n"
<< " 3: Input int8, Weight int8, Output int8)\n"
<< "arg3: tensor layout (0: Input[N, Hi, Wi, C], Output[N * Ho * Wo, Y * X * C])\n"
<< "arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],\n"
<< " 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])\n"
<< "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
......@@ -114,11 +116,9 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
return pass ? 0 : 1;
};
// Image To Column
if(rearrange_op == RearrangeOp::ImageToColumn)
{
// NHWC
if(layout == ConvLayout::NHWC)
if(layout == ConvLayout::GNHWC)
{
if(num_dim_spatial == 1)
{
......@@ -178,11 +178,70 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
}
}
}
else if(layout == ConvLayout::NHWGC)
{
if(num_dim_spatial == 1)
{
if(data_type == DataType::F32_F32)
{
return profile(I1, NWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, NWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, NWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, NWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(num_dim_spatial == 2)
{
if(data_type == DataType::F32_F32)
{
return profile(I2, NHWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, NHWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, NHWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, NHWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
else if(num_dim_spatial == 3)
{
if(data_type == DataType::F32_F32)
{
return profile(I3, NDHWGC{}, F32{}, F32{}, ImageToColumn{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, NDHWGC{}, F16{}, F16{}, ImageToColumn{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, NDHWGC{}, BF16{}, BF16{}, ImageToColumn{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, NDHWGC{}, INT8{}, INT8{}, ImageToColumn{});
}
}
}
}
else if(rearrange_op == RearrangeOp::ColumnToImage)
{
// NHWC
if(layout == ConvLayout::NHWC)
if(layout == ConvLayout::GNHWC)
{
if(num_dim_spatial == 1)
{
......@@ -242,6 +301,66 @@ int profile_conv_tensor_rearrange(int argc, char* argv[])
}
}
}
else if(layout == ConvLayout::NHWGC)
{
if(num_dim_spatial == 1)
{
if(data_type == DataType::F32_F32)
{
return profile(I1, NWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I1, NWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I1, NWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I1, NWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(num_dim_spatial == 2)
{
if(data_type == DataType::F32_F32)
{
return profile(I2, NHWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I2, NHWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I2, NHWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I2, NHWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
else if(num_dim_spatial == 3)
{
if(data_type == DataType::F32_F32)
{
return profile(I3, NDHWGC{}, F32{}, F32{}, ColumnToImage{});
}
else if(data_type == DataType::F16_F16)
{
return profile(I3, NDHWGC{}, F16{}, F16{}, ColumnToImage{});
}
else if(data_type == DataType::BF16_BF16)
{
return profile(I3, NDHWGC{}, BF16{}, BF16{}, ColumnToImage{});
}
else if(data_type == DataType::INT8_INT8)
{
return profile(I3, NDHWGC{}, INT8{}, INT8{}, ColumnToImage{});
}
}
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
......
profiler/src/profile_grouped_gemm.cpp
View file @ 9684677a
......@@ -27,6 +27,8 @@ enum struct GemmDataType
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
F8_F16_F16, // 4
F16_F8_F16, // 5
};
#define OP_NAME "grouped_gemm"
......@@ -56,7 +58,7 @@ int profile_grouped_gemm(int argc, char* argv[])
{
std::cout
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n"
<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: fp8@fp6; 5: f16@f8)\n"
<< "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n"
<< " 1: A[m, k] * B[n, k] = C[m, n];\n"
<< " 2: A[k, m] * B[k, n] = C[m, n];\n"
......@@ -169,6 +171,46 @@ int profile_grouped_gemm(int argc, char* argv[])
StrideCs,
kbatch);
}
else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_grouped_gemm_impl<ck::f8_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
time_kernel,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatch);
}
else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_grouped_gemm_impl<ck::half_t,
ck::f8_t,
ck::half_t,
float,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
time_kernel,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatch);
}
else
{
throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
......
script/cmake-ck-dev.sh
View file @ 9684677a
......@@ -8,8 +8,7 @@ MY_PROJECT_SOURCE=$1
cmake \
-D CMAKE_PREFIX_PATH=/opt/rocm \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_CXX_FLAGS="-std=c++17 -O3 -ftemplate-backtrace-limit=0 -fPIE -Wno-gnu-line-marker \
-save-temps=$PWD" \
-D CMAKE_CXX_FLAGS="-std=c++17 -O3 -ftemplate-backtrace-limit=0 -fPIE -Wno-gnu-line-marker" \
-D CMAKE_BUILD_TYPE=Release \
-D BUILD_DEV=ON \
-D GPU_TARGETS="gfx908;gfx90a;gfx940" \
......
script/redis-cli.conf 0 → 100644
View file @ 9684677a
fips = no
setuid = root
setgid = root
pid = /var/run/stunnel.pid
debug = 7
options = NO_SSLv2
options = NO_SSLv3
[redis-cli]
client = yes
accept = 127.0.0.1:6379
script/sccache_wrapper.sh 0 → 100755
View file @ 9684677a
#!/bin/bash
set -e
COMPILERS_HASH_DIR=${COMPILERS_HASH_DIR:-"/tmp/.sccache"}
SCCACHE_EXTRAFILES=${SCCACHE_EXTRAFILES:-"${COMPILERS_HASH_DIR}/rocm_compilers_hash_file"}
SCCACHE_BIN=${SCCACHE_BIN:-"${SCCACHE_INSTALL_LOCATION}/sccache"}
ENFORCE_REDIS="false"
while [ "$1" != "" ];
do
case $1 in
--enforce_redis )
shift; ENFORCE_REDIS="true" ;;
--no-hipcc )
shift ;;
*)
break ;;
esac
done
setup_rocm_compilers_hash_file() {
mkdir -p "$COMPILERS_HASH_DIR"
HIPCC_MD5="$(md5sum "${ROCM_PATH}/bin/hipcc")"
pushd "${ROCM_PATH}/amdgcn/bitcode"
DEVICELIBS_BITCODES_MD5="$(find . -type f -exec md5sum {} \; | sort | md5sum)"
popd
HIPCC_HASH_VALUE="${HIPCC_MD5%% *}"
DEVICELIBS_BITCODES_HASH_VALUE="${DEVICELIBS_BITCODES_MD5%% *}"
# MD5 checksums of clang and clang-offload-bundler cannot be used since they will keep changing
# if the ROCM_PATH changes, ie; for every mainline build.
# This is because ROCM_PATH gets encoded into the clang/clang-offload-bundler binaries as part
# of RPATH.
# The versions themselves contain the commit hash of the compiler repo at the time of building.
# Hence, this should be a viable alternative to using the binary checksum itself.
CLANG_VERSION="$("${ROCM_PATH}/llvm/bin/clang" --version | head -n 1)"
CLANG_OFFLOAD_BUNDLER_VERSION="$("${ROCM_PATH}/llvm/bin/clang-offload-bundler" --version | head -n 1)"
printf '%s: %s\n' 'clang version' "${CLANG_VERSION}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'clang-offload-bundler version' "${CLANG_OFFLOAD_BUNDLER_VERSION}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'hipcc md5sum' "${HIPCC_HASH_VALUE}" | tee -a "$SCCACHE_EXTRAFILES"
printf '%s: %s\n' 'devicelibs bitcode md5sum' "${DEVICELIBS_BITCODES_HASH_VALUE}" | tee -a "$SCCACHE_EXTRAFILES"
echo "sccache-wrapper: compilers hash file set up at ${SCCACHE_EXTRAFILES}"
cat "$SCCACHE_EXTRAFILES"
}
if [ "${ENFORCE_REDIS}" == "true" ]; then
if [ -z "${SCCACHE_REDIS}" ]; then
echo "SCCACHE_REDIS not set. Not wrapping compilers with sccache."
exit 10
else
response=$(redis-cli -u ${SCCACHE_REDIS} ping) || true
if [ "${response}" != "PONG" ]; then
echo "Redis server unreachable. Not wrapping compilers with sccache."
exit 20
fi
fi
fi
setup_rocm_compilers_hash_file
$SCCACHE_BIN --version
$SCCACHE_BIN --start-server
test/contraction/test_contraction.cpp
View file @ 9684677a
......@@ -10,9 +10,12 @@
#include <gtest/gtest.h>
#include "profiler/profile_contraction_impl.hpp"
#include "profiler/profile_contraction_utils.hpp"
using F32 = float;
using F64 = double;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using F64 = double;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
......@@ -20,49 +23,49 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale;
struct MemoryParams
struct Dimensions
{
std::vector<ck::index_t> M;
std::vector<ck::index_t> N;
std::vector<ck::index_t> K;
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
};
template <typename Tuple>
class TestContraction : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CDLayout = std::tuple_element_t<2, Tuple>;
using DataType = std::tuple_element_t<3, Tuple>;
using DTupleDataType = std::tuple_element_t<4, Tuple>;
using CDElementOp = std::tuple_element_t<5, Tuple>;
std::vector<MemoryParams> list_of_memory_params = {{{32, 32},
{32, 32},
{32, 32},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1}},
{{16, 16},
{32, 32},
{16, 16},
{4096, 256, 16, 1},
{16, 1, 8192, 256},
{16384, 1024, 32, 1},
{16384, 1024, 32, 1}}};
std::vector<ck::index_t> init_methods = {0, 1, 2};
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CDLayout = std::tuple_element_t<2, Tuple>;
using DataType = std::tuple_element_t<3, Tuple>;
using DTupleDataType = std::tuple_element_t<4, Tuple>;
using ComputeDataType = std::tuple_element_t<5, Tuple>;
using CDElementOp = std::tuple_element_t<6, Tuple>;
std::vector<Dimensions> dimension_list = {{{32, 32}, {32, 32}, {32, 32}},
{{16, 16}, {32, 32}, {16, 16}}};
std::vector<ck::index_t> init_methods = {1, 2};
std::unique_ptr<CDElementOp> p_cd_element_op;
void Run()
{
for(auto& memory_params : list_of_memory_params)
for(auto& dimension_params : dimension_list)
{
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
const auto& M = dimension_params.M;
const auto& N = dimension_params.N;
const auto& K = dimension_params.K;
assign_default_strides(ALayout{}, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(BLayout{}, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(CDLayout{}, StridesC, {M[0], M[1], N[0], N[1]});
assign_default_strides(CDLayout{}, StridesD, {M[0], M[1], N[0], N[1]});
for(const ck::index_t init_method : init_methods)
{
bool pass =
......@@ -70,19 +73,20 @@ class TestContraction : public ::testing::Test
BLayout,
CDLayout,
DataType,
ComputeDataType,
DTupleDataType,
CDElementOp>(true /*do_verification*/,
init_method,
false /*do_logs*/,
false /*time_kernel*/,
*p_cd_element_op,
memory_params.M,
memory_params.N,
memory_params.K,
memory_params.StridesA,
memory_params.StridesB,
memory_params.StridesC,
memory_params.StridesD);
dimension_params.M,
dimension_params.N,
dimension_params.K,
StridesA,
StridesB,
StridesC,
StridesD);
EXPECT_TRUE(pass);
}
}
......@@ -99,24 +103,18 @@ class TestContractionBilinear : public TestContraction<Tuple>
{
};
#define ALL_LAYOUT_COMBINATIONS(dt, tuple_dt, compute_dt, op) \
std::tuple<Row, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Row, Col, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Col, Row, dt, tuple_dt, compute_dt, op>
using BilinearKernelTypes =
::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Col, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Col, Row, F64, ck::Tuple<F32>, Bilinear>>;
using ScaleKernelTypes = ::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F64, ck::Tuple<>, Scale>>;
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F64, Bilinear)>;
using ScaleKernelTypes = ::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F64, Scale)>;
TYPED_TEST_SUITE(TestContractionBilinear, BilinearKernelTypes);
TYPED_TEST_SUITE(TestContractionScale, ScaleKernelTypes);
......@@ -136,3 +134,46 @@ TYPED_TEST(TestContractionScale, scale)
this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run();
}
template <typename Tuple>
class TestContractionScaleMixedPrecision : public TestContraction<Tuple>
{
};
template <typename Tuple>
class TestContractionBilinearMixedPrecision : public TestContraction<Tuple>
{
};
using BilinearKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, BF16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<F16>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<BF16>, F32, Bilinear)>;
using ScaleKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F16, Scale),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, BF16, Scale),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<>, F32, Scale)>;
TYPED_TEST_SUITE(TestContractionBilinearMixedPrecision, BilinearKernelTypesMixedPrecision);
TYPED_TEST_SUITE(TestContractionScaleMixedPrecision, ScaleKernelTypesMixedPrecision);
TYPED_TEST(TestContractionBilinearMixedPrecision, bilinear)
{
this->p_cd_element_op = std::make_unique<Bilinear>(1.f, 1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Bilinear>(-0.5f, 0.5f);
this->Run();
}
TYPED_TEST(TestContractionScaleMixedPrecision, scale)
{
this->p_cd_element_op = std::make_unique<Scale>(1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run();
}
test/contraction/test_contraction_interface.cpp
View file @ 9684677a
......@@ -34,11 +34,11 @@ class ContractionInstanceWrapper
static constexpr ck::index_t NumDim = 2;
// clang-format off
using ContractionDeviceInstance = ck::tensor_operation::device::
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector>;
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Compute|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Data|
//#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| Type|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector, F32>;
// clang-format on
bool isSupported(std::vector<ck::index_t>& ADims,
......
test/conv_tensor_rearrange/test_conv_tensor_rearrange.cpp
View file @ 9684677a
......@@ -45,14 +45,20 @@ class TestConvTensorRearrange : public ::testing::Test
using namespace ck::tensor_layout::convolution;
using namespace ck::conv_tensor_rearrange_op;
using KernelTypes1d =
::testing::Types<std::tuple<GNWC, ImageToColumn>, std::tuple<GNWC, ColumnToImage>>;
using KernelTypes1d = ::testing::Types<std::tuple<GNWC, ImageToColumn>,
std::tuple<GNWC, ColumnToImage>,
std::tuple<NWGC, ImageToColumn>,
std::tuple<NWGC, ColumnToImage>>;
using KernelTypes2d =
::testing::Types<std::tuple<GNHWC, ImageToColumn>, std::tuple<GNHWC, ColumnToImage>>;
using KernelTypes2d = ::testing::Types<std::tuple<GNHWC, ImageToColumn>,
std::tuple<GNHWC, ColumnToImage>,
std::tuple<NHWGC, ImageToColumn>,
std::tuple<NHWGC, ColumnToImage>>;
using KernelTypes3d =
::testing::Types<std::tuple<GNDHWC, ImageToColumn>, std::tuple<GNDHWC, ColumnToImage>>;
using KernelTypes3d = ::testing::Types<std::tuple<GNDHWC, ImageToColumn>,
std::tuple<GNDHWC, ColumnToImage>,
std::tuple<NDHWGC, ImageToColumn>,
std::tuple<NDHWGC, ColumnToImage>>;
template <typename Tuple>
class TestConvTensorRearrange1d : public TestConvTensorRearrange<Tuple>
......@@ -77,16 +83,16 @@ TYPED_TEST(TestConvTensorRearrange1d, Test1D)
{
this->conv_params.clear();
this->conv_params.push_back({1, 1, 4, 1, 192, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 64, 1, 64, {3}, {14}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 1, 64, 1, 64, {1}, {7}, {3}, {1}, {0}, {0}});
this->conv_params.push_back({1, 1, 64, 1, 64, {1}, {3}, {1}, {1}, {0}, {0}});
this->conv_params.push_back({1, 2, 4, 1, 192, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 64, 1, 64, {3}, {14}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 64, 1, 64, {1}, {7}, {3}, {1}, {0}, {0}});
this->conv_params.push_back({1, 2, 64, 1, 64, {1}, {3}, {1}, {1}, {0}, {0}});
// ScalarPerVector should be 1
this->conv_params.push_back({1, 1, 4, 1, 1, {3}, {28}, {1}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 4, 1, 1, {3}, {28}, {1}, {1}, {1}, {1}});
// stride != 1
this->conv_params.push_back({1, 1, 1, 1, 4, {3}, {28}, {2}, {1}, {1}, {1}});
this->conv_params.push_back({1, 2, 1, 1, 4, {3}, {28}, {2}, {1}, {1}, {1}});
// dilation != 1
this->conv_params.push_back({1, 1, 1, 1, 4, {3}, {28}, {1}, {2}, {1}, {1}});
this->conv_params.push_back({1, 2, 1, 1, 4, {3}, {28}, {1}, {2}, {1}, {1}});
#ifdef CK_ENABLE_FP32
this->template Run<1, float, float>();
#endif
......@@ -106,13 +112,13 @@ TYPED_TEST(TestConvTensorRearrange2d, Test2D)
this->conv_params.clear();
this->conv_params.push_back(
{2, 1, 4, 1, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
{2, 2, 4, 1, 192, {3, 3}, {28, 28}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back(
{2, 1, 64, 1, 64, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
{2, 2, 64, 1, 64, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
this->conv_params.push_back({2, 1, 64, 1, 64, {1, 1}, {7, 7}, {3, 3}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back({2, 1, 64, 1, 64, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
this->conv_params.push_back(
{2, 1, 64, 1, 64, {3, 3}, {28, 28}, {2, 2}, {2, 2}, {1, 1}, {1, 1}});
{2, 2, 64, 1, 64, {3, 3}, {28, 28}, {2, 2}, {2, 2}, {1, 1}, {1, 1}});
#ifdef CK_ENABLE_FP32
this->template Run<2, float, float>();
#endif
......@@ -131,13 +137,13 @@ TYPED_TEST(TestConvTensorRearrange3d, Test3D)
{
this->conv_params.clear();
this->conv_params.push_back(
{3, 1, 16, 1, 64, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {3, 3, 3}, {0, 0, 0}, {0, 0, 0}});
{3, 2, 16, 1, 64, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {3, 3, 3}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 1, 2, 1, 64, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
{3, 2, 2, 1, 64, {3, 3, 3}, {14, 14, 3}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}});
this->conv_params.push_back(
{3, 1, 32, 1, 64, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
{3, 2, 32, 1, 64, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}});
this->conv_params.push_back(
{3, 1, 64, 1, 64, {3, 3, 3}, {14, 14, 14}, {2, 2, 2}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}});
{3, 2, 64, 1, 64, {3, 3, 3}, {14, 14, 14}, {2, 2, 2}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}});
#ifdef CK_ENABLE_FP32
this->template Run<3, float, float>();
#endif
......
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